Throughout the U.S., summertime fine particulate matter (PM_2.5) exhibits a strong temperature (T) dependence. Reducing the PM_2.5 enhancement with T could reduce the public health burden of PM_2.5 now and in the warmer future. Atmospheric models are a critical tool for probing the processes and components driving observed behaviors. In this work, we describe how observed and modeled aerosol abundance and composition vary with T in the present-day Eastern U.S., with specific attention to the two major PM_2.5 components: sulfate (SO₄^2–) and organic carbon (OC). Observations in the Eastern U.S. show an average measured summertime PM_2.5-T sensitivity of 0.67 μg/m³/K, with CMAQv5.4 regional model predictions closely matching this value. Observed SO₄^2– and OC also increase with T; however, the model has component-specific discrepancies with observations. Specifically, the model underestimates SO₄^2– concentrations and their increase with T while overestimating OC concentrations and their increase with T. Here, we explore a series of model interventions aimed at correcting these deviations. We conclude that the PM_2.5-T relationship is driven by inorganic and organic systems that are highly coupled, and it is possible to design model interventions to simultaneously address biases in PM_2.5 component concentrations as well as their responses to T.

中文翻译：

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美国东部夏季 PM2.5 的观测和模型预测中温度相关的成分

在美国各地，夏季细颗粒物 (PM _2.5 ) 表现出强烈的温度 (T) 依赖性。通过 T减少 PM _{2.5 的}增强可以减轻现在和温暖的未来PM _2.5的公共健康负担。大气模型是探测驱动观察行为的过程和组件的关键工具。在这项工作中，我们描述了观测和模拟的气溶胶丰度和成分如何随当今美国东部 T 的变化而变化，特别关注 PM _2.5的两种主要成分：硫酸盐 (SO ₄ ^2– ) 和有机碳 (OC)。美国东部的观测结果显示，夏季平均测量的 PM _2.5 -T 敏感性为 0.67 μg/m ³ /K，CMAQv5.4 区域模型预测与该值非常匹配。观察到SO ₄ ^2–和OC 也随着T 的增加而增加；然而，该模型与观察结果存在特定于组件的差异。具体来说，该模型低估了 SO ₄ ^2–浓度及其随 T 的增加，同时高估了 OC 浓度及其随 T 的增加。在这里，我们探索了一系列旨在纠正这些偏差的模型干预措施。我们的结论是，PM _2.5 -T 关系是由高度耦合的无机和有机系统驱动的，并且可以设计模型干预措施来同时解决 PM _2.5成分浓度的偏差及其对 T 的响应。